Inclusion of hemoglobin level in prognostic score provides better prognostic stratification in patients with acute promyelocytic leukemia (APL)

Bloodstream gram-negative bacterial infections in adult patients with leukemia: A retrospective review of medical records in a tertiary care hospital in Western Saudi Arabia

BACKGROUND

Patients with neutropenic cancers are at high risk of acquiring infections, especially if on chemotherapy. Gram-negative bacterial infections are associated with high mortality. This study aimed to assess clinical characteristics, outcomes, and epidemiology of gram-negative bacterial (GNB) bloodstream infections (BSI) in adult patients with leukemia.

METHODS

This single-center, retrospective study included 102 adult patients diagnosed with leukemia between 2017 and 2019. The patients' demographics, infection diagnosis, leukemia diagnosis, comorbidities, and infection outcomes were collected from electronic medical records.

RESULTS

The most common GNB were Klebsiella pneumoniae (33.3 %), Pseudomonas aeruginosa (23.5 %), and Escherichia coli (17.6 %). Additionally, 36.7 % of infections were multidrug resistant. The most common comorbidities were cardiovascular diseases (36.7 %), diabetes mellitus (33.3 %), and liver diseases (24.1 %). GNB-infected patients had a higher mortality than noninfected patients (35.3 % and 11.8 %, respectively, p = 0.005). In a multivariable analysis, patients with acute myeloid leukemia and acute lymphoid leukemia were significantly more likely to acquire GNB BSI (p = 0.01), while patients with chronic myelogenous leukemia and chronic lymphocytic leukemia had a lower likelihood of developing GNB BSI. In addition, low hemoglobin level was an independent risk factor of GNB BSI (p = 0.001). Chemotherapeutic agents showed an association with increased risk of GNB BSI.

CONCLUSIONS

Patients with acute leukemia and low hemoglobin levels have increased risk of GNB BSI, which was associated with increased mortality. Prospective studies are needed to further assess the effect of co-morbidities and chemotherapy medications on the occurrence of GNB BSI according to the type of leukemia.

Predictors of early death and clinical features in newly diagnosed patients with low-intermediate risk acute promyelocytic leukemia

Background

Although most acute promyelocytic leukemia(APL) with low-intermediate risk could survive the induction treatment, early death still a big problem to have effects on overall survival in real world.This study aimed to analyze the clinical characteristics and possible predictors of early death in newly diagnosed patients with low-intermediate-risk acute promyelocytic leukemia.

Methods

Sixty patients with newly diagnosed low/intermediate-risk APL admitted to Mianyang Central Hospital from January 2013 to December 2021 were retrospectively analyzed.

Results

Sixty patients with a median age of 46 years (range, 17-75 years) were included. Fourteen patients (23.3%) were in low-risk group, and 46 patients (76.7%) were in intermediate-risk group. Fourteen patients (23.3%) died during induction treatment. Five patients died of hemorrhage, 5 of severe infection and 4 of differentiation syndrome. Multivariate analysis showed that HGB <65g/L at diagnosis (OR=38.474, 95%CI: 2.648~558.923, P=0.008) during induction treatment was an independent risk factors for early death in low- intermediate risk APL patients. In survival group, all patients achieved complete remission, the time to achieve remission was 25.87 ± 5.02 days, the average ATO dosage was 0.16 ± 0.03 mg/kg/day. In univariate analysis, there was no statistically significant difference in time span for remission when ATO dosage was in the 0.11~0.16mg/kg/day range. Compared with patients with low-risk APL, those with intermediate-risk APL had higher white blood cell counts (at diagnosis, day 3, day 5 and peak), higher level of lactate dehydrogenase, higher percentage of bone marrow promyelocytes, more platelet transfusions during treatment, and more early deaths (P<0.05). The overall survival of intermediate-risk APL patients seemed worse than those with low-risk APL (χ=5.033, P =0.025).

Conclusions

In patients with low-intermediate risk APL, HGB <65g/L at diagnosis was an independent risk factors for early death. Remission could still be achieved at low-dose ATO without affecting the required time for low-intermediate risk APL patients. Differences in clinical characteristics were found between low-risk and intermediate-risk APL. The intermediate-risk group had higher early mortality risk than the low-risk group.

Individualized Prediction Of Stroke-Associated Pneumonia For Patients With Acute Ischemic Stroke

Background

Stroke-associated pneumonia (SAP) is a serious and common complication in stroke patients.

Purpose

We aimed to develop and validate an easy-to-use model for predicting the risk of SAP in acute ischemic stroke (AIS) patients.

Patients and methods

The nomogram was established by univariate and multivariate binary logistic analyses in a training cohort of 643 AIS patients. The prediction performance was determined based on the receiver operating characteristic curve (ROC) and calibration plots in a validation cohort (N=340). Individualized clinical decision-making was conducted by weighing the net benefit in each AIS patient by decision curve analysis (DCA).

Results

Seven predictors, including age, NIHSS score on admission, atrial fibrillation, nasogastric tube intervention, mechanical ventilation, fibrinogen, and leukocyte count were incorporated to construct the nomogram model. The nomogram showed good predictive performance in ROC analysis [AUROC of 0.845 (95% CI: 0.814-0.872) in training cohort, and 0.897 (95% CI: 0.860-0.927) in validation cohort], and was superior to the A²DS², ISAN, and PANTHERIS scores. Furthermore, the calibration plots showed good agreement between actual and nomogram-predicted SAP probabilities, in both training and validation cohorts. The DCA confirmed that the SAP nomogram was clinically useful.

Conclusion

Our nomogram may provide clinicians with a simple and reliable tool for predicting SAP based on routinely available data. It may also assist clinicians with respect to individualized treatment decision-making for patients differing in risk level.